Phage display is one of most effective molecular diversity techniques, it has been used for the selection of antibodies from diverse libraries. Several facilities (Uchicago, Utoronto, UCSF) in the world have built automated phage display robot for generation of conventional antibodies to the proteome. At NCATS, we are planning to build an automated nanobody phage display platform to identify nanobodies in a fast, high-throughput fashion. Nanobody (ca. 15kD) is the intact proprietary antigen binding domain based on single-domain antibody, such as camelid, and is approximately 10% the size of a conventional antibody (ca. 150kD). The convex paratope of nanobodies provides an opportunity to access clefts on the surfaces of protein targets which are usually unreachable to conventional antibodies. Based on our on-going analysis of reported nanobodies using deep learning convolutional neural networks at NCATS, we are planning to construct focused antigen-directed phage nanobody libraries to generate monoclonal antibodies against a specific antigen family, e.g. GPCRs. At the same time, this technique can be used for our ongoing projects on the discovery of molecular probes and therapeutics such as antibody drug conjugates (ADCs), bi-specific T-cell engagers (BiTEs), or chimeric antigen receptor (CAR)-T cells to treat various diseases. The knowledge accumulated during these projects will pave a road to optimize our deep learning algorithms which will further our knowledge of the basis of protein-protein interaction, and potentially leads to the development machine-learning guided discovery of new biomarkers and therapeutics.